Radiomics across modalities: a comprehensive review of neurodegenerative diseases.
Authors
Affiliations (3)
Affiliations (3)
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Italy; Department of Surgery and Cancer, Imperial College London, UK. Electronic address: [email protected].
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Italy.
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Italy; Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA.
Abstract
Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human observers. Despite the challenges in visually interpreting radiomic features and the computational resources required to generate them, they hold significant value in downstream automated processing. For instance, in statistical or machine learning frameworks, radiomic features enhance sensitivity and specificity, making them indispensable for tasks such as diagnosis, prognosis, prediction, monitoring, image-guided interventions, and evaluating therapeutic responses. This review explores the application of radiomics in neurodegenerative diseases, with a focus on Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis. While radiomics literature often focuses on magnetic resonance imaging (MRI) and computed tomography (CT), this review also covers its broader application in nuclear medicine, with use cases of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) radiomics. Additionally, we review integrated radiomics, where features from multiple imaging modalities are fused to improve model performance. This review also highlights the growing integration of radiomics with artificial intelligence and the need for feature standardisation and reproducibility to facilitate its translation into clinical practice.